f"got {type(client)}"
            )
        self.client: qdrant_client.QdrantClient = client
        self.collection_name = collection_name
        self.embedding_function = embedding_function
        self.content_payload_key = content_payload_key or self.CONTENT_KEY
        self.metadata_payload_key = metadata_payload_key or self.METADATA_KEY
[docs]    def add_texts(
        self,
        texts: Iterable[str],
        metadatas: Optional[List[dict]] = None,
        **kwargs: Any,
    ) -> List[str]:
        """Run more texts through the embeddings and add to the vectorstore.
        Args:
            texts: Iterable of strings to add to the vectorstore.
            metadatas: Optional list of metadatas associated with the texts.
        Returns:
            List of ids from adding the texts into the vectorstore.
        """
        from qdrant_client.http import models as rest
        ids = [uuid.uuid4().hex for _ in texts]
        self.client.upsert(
            collection_name=self.collection_name,
            points=rest.Batch(
                ids=ids,
                vectors=[self.embedding_function(text) for text in texts],
                payloads=self._build_payloads(
                    texts,
                    metadatas,
                    self.content_payload_key,
                    self.metadata_payload_key,
                ),
            ),
        )
        return ids
[docs]    def similarity_search(
        self,
        query: str,
        k: int = 4,
        filter: Optional[MetadataFilter] = None,
        **kwargs: Any,
    ) -> List[Document]:
        """Return docs most similar to query.
        Args:
            query: Text to look up documents similar to.